library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.6 ✓ dplyr 1.0.8
## ✓ tidyr 1.2.0 ✓ stringr 1.4.0
## ✓ readr 2.1.2 ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(ggmap)
## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.
## Please cite ggmap if you use it! See citation("ggmap") for details.
library(here) # for specifying directory location
## here() starts at /Users/soltoffbc/Projects/Data Visualization/exercises/raster-maps
Import 311 service requests
chi_311 <- read_csv(file = here("data", "chicago-311.csv"))
## Rows: 193299 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): sr_number, sr_short_code
## dbl (4): community_area, ward, latitude, longitude
## dttm (1): created_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
chi_311
## # A tibble: 193,299 × 7
## sr_number sr_short_code created_date community_area ward latitude
## <chr> <chr> <dttm> <dbl> <dbl> <dbl>
## 1 SR19-01209373 SGQ 2019-03-23 17:13:05 58 12 41.8
## 2 SR19-01129184 SGQ 2019-03-09 01:37:26 40 20 41.8
## 3 SR19-01130159 SGQ 2019-03-09 14:51:46 40 20 41.8
## 4 SR19-01142266 SGQ 2019-03-12 12:17:31 67 17 41.8
## 5 SR19-01142389 SGQ 2019-03-12 12:46:05 59 12 41.8
## 6 SR19-01142452 SGQ 2019-03-12 12:55:17 59 12 41.8
## 7 SR19-01137036 SGQ 2019-03-11 14:35:45 2 40 42.0
## 8 SR19-01142278 SGQ 2019-03-12 12:19:09 59 12 41.8
## 9 SR19-01142678 SGQ 2019-03-12 13:35:02 59 12 41.8
## 10 SR19-01142691 SGQ 2019-03-12 13:38:39 64 13 41.8
## # … with 193,289 more rows, and 1 more variable: longitude <dbl>
Obtain map tiles using ggmap for the city of
Chicago.
# store bounding box coordinates
chi_bb <- c(
left = -87.936287,
bottom = 41.679835,
right = -87.447052,
top = 42.000835
)
# retrieve bounding box
chicago <- get_stamenmap(
bbox = chi_bb,
zoom = 11
)
## Source : http://tile.stamen.com/terrain/11/523/760.png
## Source : http://tile.stamen.com/terrain/11/524/760.png
## Source : http://tile.stamen.com/terrain/11/525/760.png
## Source : http://tile.stamen.com/terrain/11/526/760.png
## Source : http://tile.stamen.com/terrain/11/523/761.png
## Source : http://tile.stamen.com/terrain/11/524/761.png
## Source : http://tile.stamen.com/terrain/11/525/761.png
## Source : http://tile.stamen.com/terrain/11/526/761.png
## Source : http://tile.stamen.com/terrain/11/523/762.png
## Source : http://tile.stamen.com/terrain/11/524/762.png
## Source : http://tile.stamen.com/terrain/11/525/762.png
## Source : http://tile.stamen.com/terrain/11/526/762.png
# plot the raster map
ggmap(chicago)
Generate a scatterplot of complaints about potholes in streets.
# initialize map
ggmap(chicago) +
# add layer with scatterplot
# use alpha to show density of points
geom_point(
data = filter(chi_311, sr_short_code == "PHF"),
mapping = aes(
x = longitude,
y = latitude
),
size = .25,
alpha = .05
)
## Warning: Removed 8863 rows containing missing values (geom_point).
Generate a heatmap of complaints about potholes in streets. Do you
see any unusual patterns or clusterings?
# initialize the map
ggmap(chicago) +
# add the heatmap
stat_density_2d(
data = filter(chi_311, sr_short_code == "PHF"),
mapping = aes(
x = longitude,
y = latitude,
fill = stat(level)
),
alpha = .1,
bins = 50,
geom = "polygon"
)
## Warning: Removed 8863 rows containing non-finite values (stat_density2d).
Obtain map tiles for Hyde Park using the toner map tiles
# store bounding box coordinates
hp_bb <- c(
left = -87.608221,
bottom = 41.783249,
right = -87.577643,
top = 41.803038
)
# retrieve bounding box
hyde_park <- get_stamenmap(
bbox = hp_bb,
zoom = 15,
maptype = "toner"
)
## Source : http://tile.stamen.com/toner/15/8409/12188.png
## Source : http://tile.stamen.com/toner/15/8410/12188.png
## Source : http://tile.stamen.com/toner/15/8411/12188.png
## Source : http://tile.stamen.com/toner/15/8412/12188.png
## Source : http://tile.stamen.com/toner/15/8409/12189.png
## Source : http://tile.stamen.com/toner/15/8410/12189.png
## Source : http://tile.stamen.com/toner/15/8411/12189.png
## Source : http://tile.stamen.com/toner/15/8412/12189.png
## Source : http://tile.stamen.com/toner/15/8409/12190.png
## Source : http://tile.stamen.com/toner/15/8410/12190.png
## Source : http://tile.stamen.com/toner/15/8411/12190.png
## Source : http://tile.stamen.com/toner/15/8412/12190.png
# plot the raster map
ggmap(hyde_park)
Generate a scatterplot of requests to pick up dead animals in Hyde
Park
# initialize the map
ggmap(hyde_park) +
# add a scatterplot layer
geom_point(
data = filter(chi_311, sr_short_code == "SGQ"),
mapping = aes(
x = longitude,
y = latitude
)
)
## Warning: Removed 34287 rows containing missing values (geom_point).
Session Info
sessioninfo::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.1.2 (2021-11-01)
## os macOS Monterey 12.2.1
## system aarch64, darwin20
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/Chicago
## date 2022-05-02
## pandoc 2.17.1.1 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.1.0)
## backports 1.4.1 2021-12-13 [1] CRAN (R 4.1.1)
## bit 4.0.4 2020-08-04 [1] CRAN (R 4.1.1)
## bit64 4.0.5 2020-08-30 [1] CRAN (R 4.1.0)
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##
## [1] /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library
##
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